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from trainer.cross_entropy_train import test, train
from data_proc.cross_entropy_dataset import FBanksCrossEntropyDataset, DataLoader
from utils.pt_util import restore_objects, save_model, save_objects, restore_model
from speaker import load_data_speaker
from utils.preprocessing import extract_fbanks
from models.cross_entropy_model import FBankCrossEntropyNetV2
from predictions import get_embeddings
import faiss
import numpy as np
import json
import torch
from torch import optim
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
async def train_id(
train_dataset_path: str = 'dataset-speaker-csf/fbanks-train',
test_dataset_path: str = 'dataset-speaker-csf/fbanks-test',
model_name: str = 'fbanks-net-identity',
model_layers : int = 4,
epoch: int = 2,
lr: float = 0.0005,
batch_size: int = 2,
labId: str = '',
):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import multiprocessing
kwargs = {'num_workers': multiprocessing.cpu_count(),
'pin_memory': True} if torch.cuda.is_available() else {}
try:
train_dataset = FBanksCrossEntropyDataset(train_dataset_path)
train_loader = DataLoader(
train_dataset, batch_size=batch_size, shuffle=True, **kwargs)
test_dataset = FBanksCrossEntropyDataset(test_dataset_path)
test_loader = DataLoader(
test_dataset, batch_size=batch_size, shuffle=True, **kwargs)
except:
return 'path dataset test or train is not exist'
if model_name == 'fbanks-net-identity':
model = FBankCrossEntropyNetV2(num_layers= model_layers,reduction='mean').to(device)
else:
model = None
return {"model not exist in lab"}
model_path = f'./modelDir/{labId}/log_train/{model_name}/{model_layers}/'
model = restore_model(model, model_path)
last_epoch, max_accuracy, train_losses, test_losses, train_accuracies, test_accuracies = restore_objects(
model_path, (0, 0, [], [], [], []))
start = last_epoch + 1 if max_accuracy > 0 else 0
models_path = []
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in range(start, epoch):
train_loss, train_accuracy = train(
model, device, train_loader, optimizer, epoch, 500)
test_loss, test_accuracy = test(model, device, test_loader)
print('After epoch: {}, train_loss: {}, test loss is: {}, train_accuracy: {}, '
'test_accuracy: {}'.format(epoch, train_loss, test_loss, train_accuracy, test_accuracy))
train_losses.append(train_loss)
test_losses.append(test_loss)
train_accuracies.append(train_accuracy)
test_accuracies.append(test_accuracy)
if test_accuracy > max_accuracy:
max_accuracy = test_accuracy
model_path = save_model(model, epoch, model_path)
models_path.append(model_path)
save_objects((epoch, max_accuracy, train_losses, test_losses,
train_accuracies, test_accuracies), epoch, model_path)
print('saved epoch: {} as checkpoint'.format(epoch))
train_history = {
"train_accuracies": train_accuracies,
"test_accuracies": test_accuracies,
"train_losses": train_losses,
"test_losses": test_losses,
"model_path": models_path
}
return {
'history': json.dumps(train_history)
}
async def test_id(
test_dataset_path: str = 'dataset-speaker-csf/fbanks-test' ,
model_name: str = 'fbanks-net-identity',
model_layers : int =4,
batch_size: int = 2,
labId: str = '',
):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import multiprocessing
kwargs = {'num_workers': multiprocessing.cpu_count(),
'pin_memory': True} if torch.cuda.is_available() else {}
try:
test_dataset = FBanksCrossEntropyDataset(test_dataset_path)
test_loader = DataLoader(
test_dataset, batch_size=batch_size, shuffle=True, **kwargs)
except:
return 'path dataset test is not exist'
model_folder_path = f'./modelDir/{labId}/log_train/{model_name}/{model_layers}/'
for file in os.listdir(model_folder_path):
if file.endswith(".pth"):
model_path = os.path.join(model_folder_path, file)
if model_name == 'fbanks-net-identity':
try:
model = FBankCrossEntropyNetV2(num_layers=model_layers, reduction= "mean")
cpkt = torch.load(model_path)
model.load_state_dict(cpkt)
model.to(device)
except:
print('cuda load is error')
device = torch.device("cpu")
model = FBankCrossEntropyNetV2(num_layers=model_layers,reduction= "mean")
cpkt = torch.load(model_path)
model.load_state_dict(cpkt)
model.to(device)
else:
model = None
return {"model not exist in lab"}
test_loss, accurancy_mean = test(model, device, test_loader)
print(accurancy_mean)
return {
'test_loss': test_loss,
'test_accuracy': accurancy_mean
}
async def infer_id(
speech_file_path: str = './quangnam.wav',
model_name :str = "fbanks-net-identity",
model_layers : int = 4,
num_speaker: int = 5,
labId: str = '',
):
model_folder_path = f'./modelDir/{labId}/log_train/{model_name}/{model_layers}'
for file in os.listdir(model_folder_path):
if file.endswith(".pth"):
model_path = os.path.join(model_folder_path, file)
if model_name == 'fbanks-net-identity':
try:
model = FBankCrossEntropyNetV2(num_layers=model_layers, reduction= "mean")
cpkt = torch.load(model_path)
model.load_state_dict(cpkt)
model.to(device)
except:
print('cuda load is error')
device = torch.device("cpu")
model = FBankCrossEntropyNetV2(num_layers=model_layers,reduction= "mean")
cpkt = torch.load(model_path)
model.load_state_dict(cpkt)
model.to(device)
else:
model = None
return {"model not exist in lab"}
fbanks = extract_fbanks(speech_file_path)
embeddings = get_embeddings(fbanks, model)
mean_embeddings = np.mean(embeddings, axis=0)
mean_embeddings = mean_embeddings.reshape((1, -1))
rs = load_data_speaker(labId)
encodes = []
person_ids = []
for key, vectors in rs.items():
for emb, vector in vectors.items():
encodes.append(np.array(vector, dtype=np.float32))
person_ids.append(key)
encodes = np.vstack(encodes).astype(np.float32)
index = faiss.IndexFlatL2(encodes.shape[1])
index.add(encodes)
distances, indices = index.search(mean_embeddings, num_speaker)
rs_speaker = []
for i in range(num_speaker):
# rs_speaker.append(f"speaker {i+1}: {person_ids[indices[0][i]]}, distances: {distances[0][i]}")
rs_speaker.append({
"speaker_name": person_ids[indices[0][i]],
"distance": str(distances[0][i])
})
return {
'result': rs_speaker
}
if __name__ == '__main__':
result = infer_id()
print(result) |